diff --git a/vllm_ascend/ops/fused_moe.py b/vllm_ascend/ops/fused_moe.py
index 86974770c5..0374d61e07 100644
--- a/vllm_ascend/ops/fused_moe.py
+++ b/vllm_ascend/ops/fused_moe.py
@@ -172,8 +172,6 @@ def fused_experts_with_mc2(
             npu_wait_tensor(shared_gate_up, expand_x)
             shared_act = shared_experts.act_fn(shared_gate_up)
 
-    w1 = w1.transpose(1, 2)
-
     group_list = expert_token_nums.to(torch.int64)
     gate_up_out_list = torch_npu.npu_grouped_matmul(
         x=[expand_x],
@@ -189,7 +187,6 @@ def fused_experts_with_mc2(
     gate_up_out = torch.cat(gate_up_out_list, dim=0)
     gate_up_out = torch_npu.npu_swiglu(gate_up_out)
 
-    w2 = w2.transpose(1, 2)
     down_out_list = torch_npu.npu_grouped_matmul(
         x=[gate_up_out],
         weight=[w2],
@@ -266,7 +263,6 @@ def apply_mlp(hidden_states_wrapper: List[torch.Tensor],
     assert len(hidden_states_wrapper) == 1
     hidden_states = hidden_states_wrapper.pop()
 
-    w1 = w1.transpose(1, 2)
     hidden_states = torch_npu.npu_grouped_matmul(
         x=[hidden_states],
         weight=[w1],
@@ -369,7 +365,6 @@ def fused_experts_with_all2all(
             expanded_expert_idx, num_experts)
         expert_tokens = expert_tokens.to(torch.int64)
 
-    w1 = w1.transpose(1, 2)
     gate_up_out_list = torch_npu.npu_grouped_matmul(
         x=[hidden_states],
         weight=[w1],
@@ -611,7 +606,6 @@ def fused_experts_moge(
         0, sorted_topk_ids).unsqueeze(-1)
     group_list = num_tokens_per_expert.cumsum(dim=0).to(torch.int64)
 
-    w1 = w1.transpose(1, 2)
     gate_up_out = torch_npu.npu_grouped_matmul(
         x=[sorted_hidden_states],
         weight=[w1],
@@ -628,7 +622,6 @@ def fused_experts_moge(
         gate_up_out = torch_npu.npu_swiglu(gate_up_out)
     gate_up_out *= topk_scales
 
-    w2 = w2.transpose(1, 2)
     down_out_list = torch_npu.npu_grouped_matmul(
         x=[gate_up_out],
         weight=[w2],
@@ -760,7 +753,6 @@ def fused_experts(
             expanded_expert_idx, num_experts)
         expert_tokens = expert_tokens.to(torch.int64)
 
-    w1 = w1.transpose(1, 2)
     gate_up_out_list = torch_npu.npu_grouped_matmul(
         x=[sorted_hidden_states],
         weight=[w1],
@@ -774,7 +766,6 @@ def fused_experts(
     gate_up_out = torch.cat(gate_up_out_list, dim=0)
     gate_up_out = torch_npu.npu_swiglu(gate_up_out)
 
-    w2 = w2.transpose(1, 2)
     down_out_list = torch_npu.npu_grouped_matmul(
         x=[gate_up_out],
         weight=[w2],
@@ -1003,12 +994,13 @@ def __init__(self, moe: FusedMoEConfig = None):
     def process_weights_after_loading(self, layer):
         super(UnquantizedFusedMoEMethod,
               self).process_weights_after_loading(layer)
-        layer.w13_weight = torch.nn.Parameter(self._maybe_pad_weight(
-            layer.w13_weight.data),
-                                              requires_grad=False)
-        layer.w2_weight = torch.nn.Parameter(self._maybe_pad_weight(
-            layer.w2_weight.data),
-                                             requires_grad=False)
+        w13_data = self._maybe_pad_weight(layer.w13_weight.data).transpose(
+            1, 2).contiguous()
+        layer.w13_weight = torch.nn.Parameter(w13_data, requires_grad=False)
+
+        w2_data = self._maybe_pad_weight(layer.w2_weight.data).transpose(
+            1, 2).contiguous()
+        layer.w2_weight = torch.nn.Parameter(w2_data, requires_grad=False)
 
     def apply(
         self,